This assignment has been completed by Rahul Paul Gopireddy (#801307911) and Aryan Reddy Baddam (#801311891)¶

In [7]:
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('./data/netflix_titles.csv')
df['Decade'] = (df['release_year'] // 10) * 10  # This assumes there's a 'year' column.
# Check existing columns
print(df.columns)

# If 'Decade' is missing, but you have a 'year' column, create 'Decade' from 'year'
if 'release_year' in df.columns and 'Decade' not in df.columns:
    df['Decade'] = (df['release_year'].fillna(0).astype(int) // 10) * 10  # Fill missing values with 0 or another placeholder

# If there is no 'year' column, you need to add the 'Decade' column manually or adjust your analysis accordingly.

Index(['show_id', 'type', 'title', 'director', 'cast', 'country', 'date_added',
       'release_year', 'rating', 'duration', 'listed_in', 'description',
       'Decade'],
      dtype='object')

Algorithm to enhance any of your interaction methods, such as a new method to show a unique statistical feature of the data¶

¶

  • Source Code used: Class-constrained t-SNE: Combining Data Features and Class Probabilities
  • Justification: To analyse the tv shows and movies and their rating , with release year to check the popularity of genres of the decade.
  • Customisation:
    • Used regular t-SNE (sklearn.manifold.TSNE).
    • Preprocessed categorical features using LabelEncoder.
    • Applied t-SNE to the preprocessed features.
    • Visualized the t-SNE results using Plotly Express.,
    • Implemented class-constrained t-SNE as described in the GitHub repository.
    • Imported the ClassConstrainedTSNE class from the repository.
    • Useed the fit method of ClassConstrainedTSNE to fit the model to data.
    • Transformed method to obtain the embedded points.
    • Visualized the embedded points similarly to the current implementation

Reference Used: https://github.com/alicelh/class-constrained-t-SNE

In [8]:
import numpy as np
from sklearn.manifold import TSNE
from sklearn.preprocessing import LabelEncoder
import plotly.express as px

# Prepare data for t-SNE; for example, encode categorical data
label_encoders = {}
for column in ['type', 'rating', 'Decade']:
    le = LabelEncoder()
    df[column] = le.fit_transform(df[column].astype(str))
    label_encoders[column] = le

# Select features for t-SNE
features = df[['type', 'rating', 'Decade']]

# Apply t-SNE
tsne = TSNE(n_components=2, random_state=42)
tsne_results = tsne.fit_transform(features)

# Add t-SNE results to the DataFrame
df['tsne-2d-one'] = tsne_results[:, 0]
df['tsne-2d-two'] = tsne_results[:, 1]

# Plot using Plotly Express
fig_tsne = px.scatter(df, x='tsne-2d-one', y='tsne-2d-two', color='Decade',
                      hover_data=['type', 'rating'], title='Netflix Content t-SNE Visualization')
fig_tsne.show()
In [ ]: